Stocks and investors are turning their attention to how the next technology cycle matures after a long wait, according to a discussion on Motley Fool Hidden Gems Investing recorded July 10, 2026. The episode compared today’s artificial intelligence build-out to earlier waves—from personal computers and the early internet to mobile—and argued that adoption tends to accelerate only after infrastructure and use cases become affordable and integrated.
Contributors Travis Hoium, Lou Whiteman and Andy Cross said enterprise demand has been a key driver in the latest AI cycle, drawing parallels to how companies helped establish PC and internet ecosystems decades earlier. They also highlighted risks tied to shifting customer needs, chip economics, and the potential for AI-related automation to pressure software providers’ traditional revenue models.
Key takeaways
- Tech adoption tends to lag infrastructure: Early PC and internet penetration rose gradually from the 1980s through the 1990s, and the same “foundation before payoff” dynamic may apply to AI.
- Enterprise is leading AI spending: The panel pointed to higher-cost AI deployments in business settings as a major source of growth compared with low-cost consumer subscriptions.
- Infrastructure investment can create winners but also dilution of returns: Large capital requirements can favor established platforms, while modular competition can pressure margins.
- Unknowns are likely to drive dispersion: The group focused on uncertainties around robotics, next product cycles for major consumer franchises, and how customers allocate chip spend.
What history suggests about AI adoption
Cross framed the 1980s and 1990s as a period that combined two forces: easing monetary conditions and rapid technology change. He said the Fed’s late-1970s and early-1980s move to bring down inflation-related rates was painful in the short term but ultimately supported broad investor returns during that decade.
On the technology side, the panel emphasized that adoption rarely occurs instantly. Cross cited household PC penetration growing from low single digits in the early 1980s to a much higher share by the late 1990s—arguing that even when products exist, real productivity benefits can take time to reach scale. Whiteman added that early tools often looked like “novelties” before becoming productivity systems once affordability and practical applications improved.
The panel extended the lesson to AI, saying that some spending will be ineffective while the remainder creates the base for a future “virtuous cycle.” They compared the build-out to earlier technology transitions that required multiple layers to work together—hardware, software and distribution—before markets rewarded the new category.
Enterprise demand, and why it matters
Hoium and Cross discussed how the current AI rollout resembles earlier enterprise-led technology adoption. Cross said early business uptake for PCs drove ecosystem formation, and that modern AI growth similarly appears tied to corporate use cases rather than purely consumer behavior.
Whiteman focused on the ROI hurdle. He said businesses can justify AI investment with quantifiable outcomes, while consumers may adopt technology because it is convenient—but not always because it clearly delivers a return. The implication for investors, according to the discussion, is that the companies most directly tied to measurable enterprise adoption may see clearer demand signals even if consumer usage grows more slowly.
From the internet bubble to AI’s next phase
The panel drew a parallel between the early 2000s internet era—after the late-1990s bubble matured—and the likelihood that AI will produce a mix of outcomes rather than a straight line upward. Cross said the market in that period eventually separated winners from commoditized players and that large platforms continued to build on earlier infrastructure despite severe drawdowns.
Whiteman argued the internet’s cost reduction for information distribution changed both markets and expectations, creating a new norm that “the world comes to me.” He suggested AI could eventually play a similar role—potentially turning today’s hyperscaler spending into a broader utility-like foundation, while value creation shifts toward companies that deploy models effectively into productivity workflows.
Unknowns investors are watching: robotics, product cycles and chip allocation
The episode also zeroed in on “unknowns,” the items investors often cannot price in fully until they become clearer. With Nvidia as an example, Whiteman said one known uncertainty is whether robotics will be a major next platform and whether Nvidia’s AI-focused advantage could face pressures from custom chips and competition. Cross agreed robotics could be a key upside, but said it remains “ill-defined,” meaning the timeline and market size are hard to forecast.
The discussion extended to Apple, where Cross said the company’s challenge is finding the “next form factor” after the iPhone’s ecosystem was accelerated by the app store. Whiteman offered a more cautious view, arguing that while Apple’s current business is profitable, consumer “next narratives” have been missing, and he expects more iteration—potentially aided by AI—to determine whether upgrades re-accelerate.
For Taiwan Semiconductor Manufacturing, Cross flagged customer behavior as the crucial variable, warning that if buyers shift away from TSMC’s leading position, the impact would be material. Whiteman added that government support in the U.S. and potentially Europe is part of the equation, noting national industrial policies that could alter incentives for building and operating fabs.
Finally, the panel discussed Intuit as a smaller stock idea with multiple risks and potential catalysts. Cross said the company’s decline could reflect market concerns about existential threats from AI that could automate tax preparation and accounting workflows. He also argued that acquisition strategy can become a problem if spending extends beyond the core market and does not translate into sustainable earnings growth.
Stocks on the radar and what the panel emphasized
In the episode’s stock segment, Andy Cross highlighted Primo Brands, describing it as a bottled-water and hydration distribution business with a large retail footprint, natural spring sources and delivery reach across the U.S. and Canada. Cross characterized the valuation as attractive and pointed to the company working through a recent merger while aiming for mid-to-high single digit annualized returns, according to the conversation.
For airlines, the panel pointed to Delta Air Lines as a near-term reference point for broader demand durability. The discussion said Delta posted a earnings beat and attributed performance to fuel conditions relative to initial fears and strong revenue streams including upgrades, corporate travel, cargo and maintenance work for other fleets. The takeaway offered by the panel was that passengers willing to pay appear to be holding up, though they suggested competitive dynamics could differ by carrier.
For investors seeking “hidden gem” opportunities, Cross and Whiteman emphasized underappreciated assets and durable businesses outside the center of large-cap tech attention, including an example of a Canadian retailer expanding into the U.S. Whiteman also said financial stocks and dividend-oriented names can offer a different risk-return profile if growth remains steady.
Looking ahead, the panel’s framework suggests investors should watch how quickly enterprise AI deployments translate into measurable productivity gains, whether competition from custom silicon intensifies, and how next-cycle narratives for major consumer platforms develop. With the episode recorded in mid-July, the near-term focus for the market remains on upcoming quarterly earnings and data releases that clarify demand, margins and the pace of technology spending.
